karashiiro/gpt-oss-20b-uwufied

TEXT GENERATIONConcurrent Unit Cost:1Model Size:20BQuant:FP8Context Size:32kPublished:Nov 8, 2025License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

karashiiro/gpt-oss-20b-uwufied is a 20 billion parameter GPT-OSS model developed by karashiiro, fine-tuned using Unsloth and Huggingface's TRL library. This model is specifically designed to generate text in an "uwu-fied" style, characterized by exaggerated cuteness and internet slang, as demonstrated in its sample outputs. It offers a 32768 token context length and is suitable for creative text generation where a distinct, playful tone is desired.

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Model Overview

karashiiro/gpt-oss-20b-uwufied is a 20 billion parameter language model, fine-tuned by karashiiro from the unsloth/gpt-oss-20b-unsloth-bnb-4bit base model. This fine-tuning process leveraged Unsloth for 2x faster training and Huggingface's TRL library. The model's primary distinguishing feature is its unique "uwu-fied" output style, incorporating internet slang, emojis, and a generally cutesy tone.

Key Capabilities

  • "Uwu-fied" Text Generation: Produces highly stylized text with characteristic internet slang, emojis, and an exaggerated cute tone.
  • Fast Fine-tuning: Benefits from Unsloth's optimization for quicker training times.
  • Large Context Window: Supports a context length of 32768 tokens, allowing for processing and generating longer texts while maintaining its unique style.

Good For

  • Creative Writing & Roleplay: Ideal for generating dialogue, stories, or character responses in a distinct, playful, and internet-culture-infused style.
  • Novelty Applications: Suitable for projects requiring a unique and engaging textual output that stands out from standard LLM responses.
  • Experimentation with Stylistic Generation: Developers interested in exploring models with highly specific and non-standard output styles will find this model useful.

Users should be aware that while the model is available in HF format, GGUF is recommended if loading issues are encountered.